{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top-label calibration for outputs of ML models\n",
    "\n",
    "The goal of this notebook is to present :class:`mapie.calibration.TopLabelCalibrator` by comparing it to the method presented in the paper for Top-label calibration [1].\n",
    "\n",
    "[1] Gupta, Chirag, and Aaditya K. Ramdas. \"Top-label calibration and multiclass-to-binary reductions.\" arXiv preprint arXiv:2107.08353 (2021)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'df-posthoc-calibration'...\n",
      "remote: Enumerating objects: 309, done.\u001b[K\n",
      "remote: Counting objects: 100% (197/197), done.\u001b[K\n",
      "remote: Compressing objects: 100% (105/105), done.\u001b[K\n",
      "remote: Total 309 (delta 101), reused 183 (delta 89), pack-reused 112\u001b[K\n",
      "Receiving objects: 100% (309/309), 60.76 MiB | 8.86 MiB/s, done.\n",
      "Resolving deltas: 100% (132/132), done.\n",
      "Note: switching to '109da93c1487cb38ee51fcac47088cdd29854999'.\n",
      "\n",
      "You are in 'detached HEAD' state. You can look around, make experimental\n",
      "changes and commit them, and you can discard any commits you make in this\n",
      "state without impacting any branches by switching back to a branch.\n",
      "\n",
      "If you want to create a new branch to retain commits you create, you may\n",
      "do so (now or later) by using -c with the switch command. Example:\n",
      "\n",
      "  git switch -c <new-branch-name>\n",
      "\n",
      "Or undo this operation with:\n",
      "\n",
      "  git switch -\n",
      "\n",
      "Turn off this advice by setting config variable advice.detachedHead to false\n",
      "\n",
      "HEAD is now at 109da93 Create README.md\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/AIgen/df-posthoc-calibration\n",
    "!cd df-posthoc-calibration && git checkout 109da93c1487cb38ee51fcac47088cdd29854999\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/scikit-learn-contrib/MAPIE/blob/master/notebooks/classification/top_label_calibration.ipynb)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from mapie.calibration import TopLabelCalibrator\n",
    "from mapie.metrics.calibration import top_label_ece\n",
    "\n",
    "import sys\n",
    "\n",
    "sys.path.append(\"df-posthoc-calibration/\")\n",
    "import assessment\n",
    "import calibration \n",
    "\n",
    "random_state = 20\n",
    "\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Creating a classification dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "centers = [(0, 3.5), (-2, 0), (2, 0)]\n",
    "covs = [np.eye(2), np.eye(2)*2, np.diag([5, 1])]\n",
    "x_min, x_max, y_min, y_max, step = -6, 8, -6, 8, 0.1\n",
    "n_samples = 1000\n",
    "n_classes = 3\n",
    "np.random.seed(42)\n",
    "X = np.vstack([\n",
    "    np.random.multivariate_normal(center, cov, n_samples)\n",
    "    for center, cov in zip(centers, covs)\n",
    "])\n",
    "y = np.hstack([np.full(n_samples, i) for i in range(1, n_classes+1)])\n",
    "\n",
    "X_train_cal, X_test, y_train_cal, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=random_state\n",
    ")\n",
    "X_train, X_cal, y_train, y_cal = train_test_split(\n",
    "    X_train_cal, y_train_cal, test_size=0.25, random_state=random_state\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Fitting a classifier on the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(random_state=20)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomForestClassifier(random_state=random_state)\n",
    "clf.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computing the prediction probabilities using the trained classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds_calib = clf.predict_proba(X_cal)\n",
    "preds_test = clf.predict_proba(X_test)\n",
    "arg_max_preds_test = clf.classes_[np.argmax(preds_test, axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.94      0.93      0.93       208\n",
      "           2       0.82      0.85      0.83       198\n",
      "           3       0.88      0.85      0.86       194\n",
      "\n",
      "    accuracy                           0.88       600\n",
      "   macro avg       0.88      0.88      0.88       600\n",
      "weighted avg       0.88      0.88      0.88       600\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = clf.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
    "                              display_labels=clf.classes_)\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Calibration of data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using method Top-Label calibration (see section 2 of paper [1])."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "points_per_bin=50\n",
    "\n",
    "# initialize recalibrator and set number of points per bins\n",
    "hb = calibration.HB_toplabel(points_per_bin=points_per_bin)\n",
    "hb.fit(preds_calib, y_cal)\n",
    "\n",
    "# get histogram binning probabilities on test data\n",
    "preds_test_hb = hb.predict_proba(preds_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using MAPIE `calibration.py`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapie_reg = TopLabelCalibrator(estimator=clf, cv=\"prefit\", calibrator=\"isotonic\")\n",
    "mapie_reg.fit(X_cal, y_cal)\n",
    "mapie_prob_preds = mapie_reg.predict_proba(X_test)\n",
    "mapie_preds = mapie_reg.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Verification that the same predictions are made.\n",
    "np.testing.assert_array_equal(mapie_preds, clf.classes_[np.nanargmax(mapie_prob_preds, axis=1)])\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Evaluating the models using ECE and Reliability diagrams introduced in [1].\n",
    "\n",
    "Note that since we use different calibration methods, the results are slightly different, however, we still find similar results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x550 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make some plots\n",
    "fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(15,5.5), constrained_layout=True)\n",
    "fig.suptitle('Top-label reliability diagrams', fontsize=30)\n",
    "\n",
    "assessment.toplabel_reliability_diagram(y_test, preds_test, ax=ax[0])\n",
    "ax[0].set_title(\"Base ResNet-50 model \\n (Top-label ECE = {:.3f})\".format(top_label_ece(y_test, np.nanmax(preds_test, axis=1), arg_max_preds_test)))\n",
    "\n",
    "assessment.toplabel_reliability_diagram(y_test, preds_test_hb, arg_max_preds_test, ax=ax[1], color='g')\n",
    "ax[1].set_title(\"ResNet-50 + histogram binning calibration\\n (Top-label-ECE = {:.3f})\".format(top_label_ece(y_test, preds_test_hb, arg_max_preds_test)))\n",
    "\n",
    "assessment.toplabel_reliability_diagram(y_test, np.nanmax(mapie_prob_preds, axis=1), arg_max_preds_test, ax=ax[2], color='g')\n",
    "ax[2].set_title(\"ResNet-50 + MAPIE calibration (using Platt)\\n (Top-label-ECE = {:.3f})\".format(top_label_ece(y_test, np.nanmax(mapie_prob_preds, axis=1), arg_max_preds_test)));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "!rm -r -f df-posthoc-calibration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
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